The article discusses the development of an intelligent video surveillance system designed to identify recipients of state services among low-mobility population groups. The proposed approach is based on the use of modern computer vision and machine learning technologies, which improves the efficiency of social assistance delivery and optimizes interaction between citizens and government institutions. The system can automatically detect people with limited mobility in public places, such as state service centers, and ensure their timely support.
intelligent system, video surveillance, low-mobility population groups, state services, computer vision, machine learning, automation, social support
1. Yaryshev, S.N. Tehnologii glubokogo obucheniya i neyronnyh setey v zadachah videoanaliza / S.N. Yaryshev, V.A. Ryzhova. – Sankt-Peterburg: Universitet ITMO, 2022. – 82 s.
2. Principy postroeniya samoorganizuyuschihsya informacionno-telekommunikacionnyh sistem / A.A. Sukonschikov, A.N. Shvecov, I.A. Andrianov, D.V. Kochkin // Vestnik Cherepoveckogo gosudarstvennogo universiteta. – 2021. – № 1(100). – S. 56-67. – DOIhttps://doi.org/10.23859/1994-0637-2021-1-100-4.
3. Tihanovskiy, G.S. Issledovanie vozmozhnostey primeneniya algoritmov glubokogo obucheniya i komp'yuternogo zreniya v zadache klassifikacii gribov i yagod / G.S. Tihanovskiy, D.V. Kochkin // Vestnik Vologodskogo gosudarstvennogo universiteta. Seriya: Tehnicheskie nauki. – 2023. – № 4(22). – S. 41-46.
4. Mahonin, M.N. razrabotka sistemy raspoznavaniya tovarov na baze neyronnyh setey / M.N. Mahonin, D.V. Kochkin // Shkola prakticheskih innovaciy - inzhenernomu biznesu regiona : Materialy Vserossiyskoy nauchno-prakticheskoy konferencii pamyati professora Aleksandra Nikolaevicha Shichkova, Vologda, 19 yanvarya 2024 goda. – Vologda: Vologodskiy gosudarstvennyy universitet, 2024. – S. 177-182.
5. Mahonin, M.N. Detektor tovarov na baze neyronnoy seti / M.N. Mahonin, D.V. Kochkin // Intellektual'no-informacionnye tehnologii i intellektual'nyy biznes (INFOS-2024) : materialy Pyatnadcatoy Mezhdunarodnoy nauchno-tehnicheskoy konferencii, Vologda, 27–28 iyunya 2024 goda. – Vologda: Vologodskiy gosudarstvennyy universitet, 2024. – S. 22-25.
6. Gudfellou, I. Glubokoe obuchenie / I. Gudfellou, I. Bendzhio, A. Kurvil'. – Moskva: DMK Press, 2018. – 652 s.
7. Beyeler, M. OpenCV s pomosch'yu Python. Gotovye resheniya dlya komp'yuternogo zreniya / M. Beyeler. – Moskva: DMK Press, 2017. – 222 s.
8. Modeli i metody postroeniya neyro-nechetkih intellektual'nyh agentov v informacionno-telekommunikacionnyh sistemah / A.A. Sukonschikov, I.A. Andrianov, S.V. Dianov [i dr.]. – Kursk : Zakrytoe akcionernoe obschestvo "Universitetskaya kniga", 2021. – 152 s.
9. Selyanin, N.S. Vliyanie ispol'zovaniya avtomaticheskogo II-diagnosticheskogo modulya na effektivnost' raboty servisnyh centrov / N.S. Selyanin, D.V. Kochkin // Vestnik Vologodskogo gosudarstvennogo universiteta. Seriya: Tehnicheskie nauki. – 2024. – № 4(26). – S. 37-40.
10. Rouzbrok, A. Deep Learning for Computer Vision with Python / A. Rouzbrok. – 2017. – 1340 s.
